Toward Polymorphic Backdoor Against Semantic Communication via Intensity-Based Poisoning

Researchers created SemBugger, a polymorphic backdoor attack (a type of hidden malicious code that can change its behavior) against semantic communication (SC, a system where AI learns shared knowledge to compress and transmit information efficiently). The attack uses variable-intensity triggers to poison training data and manipulate the system into producing different malicious outputs while appearing normal, but the researchers also developed a defense mechanism using controlled noise that can resist these attacks.